Free Microsoft Azure AI Fundamentals (Updated Version) AI-901 Exam Questions

The requirement is to produce new product images based on written descriptions. This is an image generation workload, because the AI system is creating entirely new images from natural language prompts.

Why the other options are incorrect:

B . image analysis is used to examine and interpret existing images.

C . object detection is used to identify and locate objects within an existing image.

D . optical character recognition (OCR) is used to extract text from images or scanned documents.

Since the solution must generate new visual content from user-provided descriptions, the correct answer is:

A . image generation

Azure Content Understanding analysis operations are long-running operations in the Python SDK. Microsoft's Python SDK documentation states that analysis operations return a poller, and the SDK provides LROPoller types that handle polling automatically when you call .result().

Therefore, the correct workflow is to submit the PDF by calling begin_analyze(), receive a poller, and then call:

result = poller.result()

Option B is incorrect because extraction results are not read from request headers. Option C is incorrect because the requirement is to use Azure Content Understanding extraction, not build a manual OCR-only mapping pipeline. Option D is incorrect because the SDK analysis pattern is asynchronous/long-running, not a simple synchronous analyze() call that returns all extracted fields in the same request.

The SpeechConfig class is the correct choice for configuring the connection to the Azure Speech service in the Azure Speech SDK for Python. This class is used to set up essential connection parameters including:

• Subscription key - Your Azure Speech service subscription key
• Region - The Azure region where your Speech service is deployed
• Endpoint - Optional custom endpoint configuration
• Speech recognition language - The language for speech-to-text operations
• Output format - Configuration for speech synthesis output

Once you create a SpeechConfig instance with your credentials and settings, you pass it to other speech SDK classes like SpeechRecognizer (for speech-to-text) or SpeechSynthesizer (for text-to-speech) to enable real-time spoken conversations. This makes SpeechConfig the foundational configuration class for any Azure Speech SDK application.

To create an AI agent that follows a specific role and behavior, you configure system instructions. Microsoft Foundry Agent Service documentation states that agent instructions define goals, constraints, and behavior.

Option A. tokens per minute (TPM) controls throughput quota, not behavior. Option C. temperature controls response randomness/creativity, not the agent's role. Option D. max completion tokens controls response length, not the agent's role or behavioral rules.

Therefore, the correct answer is B. system instructions.

For vision-enabled Azure OpenAI / Microsoft Foundry model requests, image input can be provided by using an image URL or base64-encoded image data. Microsoft's Azure OpenAI REST API reference states that the image content part URL field can contain either a URL of the image or the base64 encoded image data. It also states that the Responses API input_image.image_url value can be a fully qualified URL or a base64 encoded image in a data URL.

Voicemail recordings are audio content. Azure Content Understanding analyzers define what type of content to process, including documents, images, audio, or video, and what elements to extract, including transcripts and structured fields.

Microsoft's custom analyzer documentation also shows an audio example based on prebuilt-audio for processing customer support call recordings, which is the same content type as voicemail recordings.

Therefore, to extract a transcript and structured information from voicemail recordings, you should use an audio analyzer.

The Microsoft responsible AI principle of inclusiveness means AI systems should be designed to empower and engage everyone, including people with different abilities, languages, and accessibility needs.

Therefore, designing the interface to support multiple languages and screen readers is an example of inclusiveness.

Why the other options are incorrect:

A . Provide explanations about how predictions are generated = Transparency C . Evaluate model outputs across demographic groups to reduce bias = Fairness D . Encrypt stored customer data and restrict access by using role-based controls = Privacy and security

Microsoft Foundry Agent Service documentation states that instructions define goals, constraints, and behavior for an agent. Therefore, instructions are used to guide how the generative AI model or agent should respond and behave.

Option A is correct because instructions can define constraints the model must follow.

Option B is correct because instructions can define the agent's role and behavior.

Options C, D, and E are incorrect because Azure region, model selection, and TPM allocation are configuration or deployment/resource settings, not purposes of prompt instructions.

To use Azure Content Understanding in Foundry Tools, Microsoft lists a Microsoft Foundry resource as a prerequisite. The documentation states that you need a Microsoft Foundry resource created in a supported region, and that the portal lists this resource under Foundry > Foundry.

The invoice scenario is also directly aligned with Content Understanding's intelligent document processing use case: Microsoft states that Content Understanding converts unstructured documents into structured data and gives invoice processing as an example.

Therefore, to extract structured data from invoices by using Azure Content Understanding in Foundry Tools, you should provision a Microsoft Foundry resource.

To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.

Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.

Why the other options are incorrect:

A . Evaluator catalog is for selecting evaluators to measure model or application outputs, not comparing LLM costs. C . Compliance relates to governance and compliance, not model cost comparison. D . Tools provides Foundry tools, not benchmarked cost comparison across models.